This paper reviews guidelines on how medical imaging analysis can be enhanced by Artificial Intelligence (AI) and Machine Learning (ML). In addition to outlining current and potential future developments, we also provide background information on chemical imaging and discuss the advantages of Explainable AI. We hypothesize that it is a matter of AI to find an invariably recurring parameter that has escaped human attention (e.g. due to noisy data). There is great potential in AI to illuminate the feature space of successful models.
|Title of host publication||Developments in AI and Machine Learning for Neuroimaging|
|Editors||A Holzinger , R Goebel, M Mengel, H Müller|
|Number of pages||14|
|Publication status||Published - 24 Jun 2020|
|Name||Lecture Notes in Computer Science|
O’Sullivan, S., Jeanquartier, F., Jean-Quartier, C., Holzinger, A., Shiebler, D., Moon, P., & Angione, C. (2020). Developments in AI and Machine Learning for Neuroimaging. In A. Holzinger , R. Goebel, M. Mengel, & H. Müller (Eds.), Developments in AI and Machine Learning for Neuroimaging (Vol. 12090, pp. 307-320). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-030-50402-1_18